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Benjakob O, Guley O, Sevin JM, Blondel L, Augustoni A, Collet M, Jouveshomme L, Amit R, Linder A, Aviram R. Wikipedia as a tool for contemporary history of science: A case study on CRISPR. PLoS One 2023; 18:e0290827. [PMID: 37703244 PMCID: PMC10499201 DOI: 10.1371/journal.pone.0290827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Accepted: 08/16/2023] [Indexed: 09/15/2023] Open
Abstract
Rapid developments and methodological divides hinder the study of how scientific knowledge accumulates, consolidates and transfers to the public sphere. Our work proposes using Wikipedia, the online encyclopedia, as a historiographical source for contemporary science. We chose the high-profile field of gene editing as our test case, performing a historical analysis of the English-language Wikipedia articles on CRISPR. Using a mixed-method approach, we qualitatively and quantitatively analyzed the CRISPR article's text, sections and references, alongside 50 affiliated articles. These, we found, documented the CRISPR field's maturation from a fundamental scientific discovery to a biotechnological revolution with vast social and cultural implications. We developed automated tools to support such research and demonstrated its applicability to two other scientific fields-coronavirus and circadian clocks. Our method utilizes Wikipedia as a digital and free archive, showing it can document the incremental growth of knowledge and the manner scientific research accumulates and translates into public discourse. Using Wikipedia in this manner compliments and overcomes some issues with contemporary histories and can also augment existing bibliometric research.
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Affiliation(s)
- Omer Benjakob
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Olha Guley
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Jean-Marc Sevin
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Leo Blondel
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Ariane Augustoni
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Matthieu Collet
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Louise Jouveshomme
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Roy Amit
- Bezalel Academy of Arts and Design, Jerusalem, Israel
| | - Ariel Linder
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
| | - Rona Aviram
- System Engineering and Evolution Dynamics, Inserm, Université Paris Cité, Paris, France
- Learning Planet Institute, Paris, France
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Yang K, Tanaka M. Crowdsourcing Knowledge Production of COVID-19 Information on Japanese Wikipedia in the Face of Uncertainty: Empirical Analysis. J Med Internet Res 2023; 25:e45024. [PMID: 37384371 PMCID: PMC10365582 DOI: 10.2196/45024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/04/2023] [Accepted: 05/29/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND A worldwide overabundance of information comprising misinformation, rumors, and propaganda concerning COVID-19 has been observed in addition to the pandemic. By addressing this data confusion, Wikipedia has become an important source of information. OBJECTIVE This study aimed to investigate how the editors of Wikipedia have handled COVID-19-related information. Specifically, it focused on 2 questions: What were the knowledge preferences of the editors who participated in producing COVID-19-related information? and How did editors with different knowledge preferences collaborate? METHODS This study used a large-scale data set, including >2 million edits in the histories of 1857 editors who edited 133 articles related to COVID-19 on Japanese Wikipedia. Machine learning methods, including graph neural network methods, Bayesian inference, and Granger causality analysis, were used to establish the editors' topic proclivity and collaboration patterns. RESULTS Overall, 3 trends were observed. Two groups of editors were involved in the production of information on COVID-19. One group had a strong preference for sociopolitical topics (social-political group), and the other group strongly preferred scientific and medical topics (scientific-medical group). The social-political group played a central role (contributing 16,544,495/23,485,683, 70.04% of bits of content and 57,969/76,673, 75.61% of the references) in the information production part of the COVID-19 articles on Wikipedia, whereas the scientific-medical group played only a secondary role. The severity of the pandemic in Japan activated the editing behaviors of the social-political group, leading them to contribute more to COVID-19 information production on Wikipedia while simultaneously deactivating the editing behaviors of the scientific-medical group, resulting in their less contribution to COVID-19 information production on Wikipedia (Pearson correlation coefficient=0.231; P<.001). CONCLUSIONS The results of this study showed that lay experts (ie, Wikipedia editors) in the fields of science and medicine tended to remain silent when facing high scientific uncertainty related to the pandemic. Considering the high quality of the COVID-19-related articles on Japanese Wikipedia, this research also suggested that the sidelining of the science and medicine editors in discussions is not necessarily a problem. Instead, the social and political context of the issues with high scientific uncertainty is more important than the scientific discussions that support accuracy.
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Affiliation(s)
- Kunhao Yang
- Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Ube, Japan
| | - Mikihito Tanaka
- Faculty of Political Science and Economics, Waseda University, Tokyo, Japan
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Schmidt M, Kircheis W, Simons A, Potthast M, Stein B. A diachronic perspective on citation latency in Wikipedia articles on CRISPR/Cas-9: an exploratory case study. Scientometrics 2023; 128:3649-3673. [PMID: 37228830 PMCID: PMC10183088 DOI: 10.1007/s11192-023-04703-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/28/2023] [Indexed: 05/27/2023]
Abstract
This paper analyzes Wikipedia's representation of the Nobel Prize winning CRISPR/Cas9 technology, a method for gene editing. We propose and evaluate different heuristics to match publications from several publication corpora against Wikipedia's central article on CRISPR and against the complete Wikipedia revision history in order to retrieve further Wikipedia articles relevant to the topic and to analyze Wikipedia's referencing patterns. We explore to what extent the selection of referenced literature of Wikipedia's central article on CRISPR adheres to scientific standards and inner-scientific perspectives by assessing its overlap with (1) the Web of Science (WoS) database, (2) a WoS-based field-delineated corpus, (3) highly-cited publications within this corpus, and (4) publications referenced by field-specific reviews. We develop a diachronic perspective on citation latency and compare the delays with which publications are cited in relevant Wikipedia articles to the citation dynamics of these publications over time. Our results confirm that a combination of verbatim searches by title, DOI, and PMID is sufficient and cannot be improved significantly by more elaborate search heuristics. We show that Wikipedia references a substantial amount of publications that are recognized by experts and highly cited, but that Wikipedia also cites less visible literature, and, to a certain degree, even not strictly scientific literature. Delays in occurrence on Wikipedia compared to the publication years show (most pronounced in case of the central CRISPR article) a dependence on the dynamics of both the field and the editor's reaction to it in terms of activity.
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Affiliation(s)
- Marion Schmidt
- German Center for Higher Education Research and Science Studies (DZHW), Berlin, Germany
| | - Wolfgang Kircheis
- Leipzig University and Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany
| | - Arno Simons
- Technische Universität Berlin, German Center for Higher Education Research and Science Studies (DZHW), Berlin, Germany
| | - Martin Potthast
- Leipzig University and Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Leipzig, Germany
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Raza S. A COVID-19 Search Engine (CO-SE) with Transformer-based architecture. HEALTHCARE ANALYTICS 2022. [PMID: 37520616 PMCID: PMC9170278 DOI: 10.1016/j.health.2022.100068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
Coronavirus disease (COVID-19) is an infectious disease, which is caused by the SARS-CoV-2 virus. Due to the growing literature on COVID-19, it is hard to get precise, up-to-date information about the virus. Practitioners, front-line workers, and researchers require expert-specific methods to stay current on scientific knowledge and research findings. However, there are a lot of research papers being written on the subject, which makes it hard to keep up with the most recent research. This problem motivates us to propose the design of the COVID-19 Search Engine (CO-SE), which is an algorithmic system that finds relevant documents for each query (asked by a user) and answers complex questions by searching a large corpus of publications. The CO-SE has a retriever component trained on the TF–IDF vectorizer that retrieves the relevant documents from the system. It also consists of a reader component that consists of a Transformer-based model, which is used to read the paragraphs and find the answers related to the query from the retrieved documents. The proposed model has outperformed previous models, obtaining an exact match ratio score of 71.45% and a semantic answer similarity score of 78.55%. It also outperforms other benchmark datasets, demonstrating the generalizability of the proposed approach.
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What drives US stock markets during the COVID-19 pandemic? A global sensitivity analysis. BORSA ISTANBUL REVIEW 2022; 22:939-960. [PMCID: PMC9283195 DOI: 10.1016/j.bir.2022.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/03/2022] [Accepted: 07/03/2022] [Indexed: 12/01/2023]
Abstract
This paper identifies robust determinants of US stock price movements in the economic shadow of the COVID-19 crisis and in the presence of model uncertainty, using several influential factors highlighted in relevant research. Our investigation performs an extreme bounds analysis (EBA), a global sensitivity framework capable of handling the problem of model uncertainty. We document that excess market returns, term spread, implied volatility, oil, Twitter-based economic uncertainty, and European and Chinese stock returns are the only variables that are robust to all possible variations in the condition set of information. The results also reveal the irrelevance of newly reported COVID-19 cases and deaths as novel drivers that contribute to the formation stock prices, thus lending support to the “psychophysical numbing” phenomenon.
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Raza S, Schwartz B, Rosella LC. CoQUAD: a COVID-19 question answering dataset system, facilitating research, benchmarking, and practice. BMC Bioinformatics 2022; 23:210. [PMID: 35655148 PMCID: PMC9160513 DOI: 10.1186/s12859-022-04751-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/26/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time. METHODS This paper introduces CoQUAD, a question-answering system that can extract answers related to COVID-19 questions in an efficient manner. There are two datasets provided in this work: a reference-standard dataset built using the CORD-19 and LitCOVID initiatives, and a gold-standard dataset prepared by the experts from a public health domain. The CoQUAD has a Retriever component trained on the BM25 algorithm that searches the reference-standard dataset for relevant documents based on a question related to COVID-19. CoQUAD also has a Reader component that consists of a Transformer-based model, namely MPNet, which is used to read the paragraphs and find the answers related to a question from the retrieved documents. In comparison to previous works, the proposed CoQUAD system can answer questions related to early, mid, and post-COVID-19 topics. RESULTS Extensive experiments on CoQUAD Retriever and Reader modules show that CoQUAD can provide effective and relevant answers to any COVID-19-related questions posed in natural language, with a higher level of accuracy. When compared to state-of-the-art baselines, CoQUAD outperforms the previous models, achieving an exact match ratio score of 77.50% and an F1 score of 77.10%. CONCLUSION CoQUAD is a question-answering system that mines COVID-19 literature using natural language processing techniques to help the research community find the most recent findings and answer any related questions.
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Affiliation(s)
- Shaina Raza
- Public Health Ontario (PHO), Toronto, ON Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
| | - Brian Schwartz
- Public Health Ontario (PHO), Toronto, ON Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
| | - Laura C. Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
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Vergoulis T, Kanellos I, Chatzopoulos S, Pla Karidi D, Dalamagas T. BIP4COVID19: Releasing impact measures for articles relevant to COVID-19. QUANTITATIVE SCIENCE STUDIES 2022. [DOI: 10.1162/qss_a_00169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Abstract
Since the beginning of the coronavirus pandemic, a large number of relevant articles have been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies or even government actions to limit the spread of the disease, and directing treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate, making the exploration of the relevant literature and the identification of useful knowledge challenging. In this work, we describe BIP4COVID19, an open data set that offers a variety of impact measures for coronavirus-related scientific articles. These measures can be exploited for the creation or extension of added-value services aiming to facilitate the exploration of the respective literature, alleviating the aforementioned issue. In the same context, as a use case, we provide a publicly accessible keyword-based search interface for COVID-19-related articles, which leverages our data to rank search results according to the calculated impact indicators.
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Affiliation(s)
| | | | - Serafeim Chatzopoulos
- IMSI, “Athena” RC, Athens, Greece
- Dept. of Informatics and Tele/tions, University of the Peloponnese, Tripolis, Greece
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Benjakob O, Aviram R, Sobel JA. Citation needed? Wikipedia bibliometrics during the first wave of the COVID-19 pandemic. Gigascience 2022; 11:6505121. [PMID: 35022700 PMCID: PMC8756189 DOI: 10.1093/gigascience/giab095] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 09/15/2021] [Accepted: 12/10/2021] [Indexed: 01/03/2023] Open
Abstract
Background With the COVID-19 pandemic’s outbreak, millions flocked to Wikipedia for updated information. Amid growing concerns regarding an “infodemic,” ensuring the quality of information is a crucial vector of public health. Investigating whether and how Wikipedia remained up to date and in line with science is key to formulating strategies to counter misinformation. Using citation analyses, we asked which sources informed Wikipedia’s COVID-19–related articles before and during the pandemic’s first wave (January–May 2020). Results We found that coronavirus-related articles referenced trusted media outlets and high-quality academic sources. Regarding academic sources, Wikipedia was found to be highly selective in terms of what science was cited. Moreover, despite a surge in COVID-19 preprints, Wikipedia had a clear preference for open-access studies published in respected journals and made little use of preprints. Building a timeline of English-language COVID-19 articles from 2001–2020 revealed a nuanced trade-off between quality and timeliness. It further showed how pre-existing articles on key topics related to the virus created a framework for integrating new knowledge. Supported by a rigid sourcing policy, this “scientific infrastructure” facilitated contextualization and regulated the influx of new information. Last, we constructed a network of DOI-Wikipedia articles, which showed the landscape of pandemic-related knowledge on Wikipedia and how academic citations create a web of shared knowledge supporting topics like COVID-19 drug development. Conclusions Understanding how scientific research interacts with the digital knowledge-sphere during the pandemic provides insight into how Wikipedia can facilitate access to science. It also reveals how, aided by what we term its “citizen encyclopedists,” it successfully fended off COVID-19 disinformation and how this unique model may be deployed in other contexts.
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Affiliation(s)
- Omer Benjakob
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, 8 bis Rue Charles V, 75004 Paris, France.,The Cohn Institute for the History and Philosophy of Science and Ideas, Humanities Faculty, Tel Aviv University, Ramat Aviv, Tel Aviv 6997801, Israel
| | - Rona Aviram
- Center for Research and Interdisciplinarity (CRI), Université de Paris, INSERM U1284, 8 bis Rue Charles V, 75004 Paris, France.,Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Jonathan Aryeh Sobel
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel.,Department of Biomedical Engineering, Julius Silver Building, Technion-IIT, Technion City, Haifa 32000, Israel
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Arroyo-Machado W, Torres-Salinas D, Robinson-Garcia N. Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics. Scientometrics 2021; 126:9267-9289. [PMID: 34658460 PMCID: PMC8507359 DOI: 10.1007/s11192-021-04167-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 09/16/2021] [Indexed: 11/28/2022]
Abstract
Altmetric indicators allow exploring and profiling individuals who discuss and share scientific literature in social media. But it is still a challenge to identify and characterize communities based on the research topics in which they are interested as social and geographic proximity also influence interactions. This paper proposes a new method which profiles social media users based on their interest on research topics using altmetric data. Social media users are clustered based on the topics related to the research publications they share in social media. This allows removing linkages which respond to social or personal proximity and identifying disconnected users who may have similar research interests. We test this method for users tweeting publications from the fields of Information Science & Library Science, and Microbiology. We conclude by discussing the potential application of this method and how it can assist information professionals, policy managers and academics to understand and identify the main actors discussing research literature in social media.
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Affiliation(s)
- Wenceslao Arroyo-Machado
- EC3 Research Group, Department of Information and Communication Sciences, Faculty of Communication and Documentation, University of Granada, Granada, Spain
| | - Daniel Torres-Salinas
- EC3 Research Group, Department of Information and Communication Sciences, Faculty of Communication and Documentation, University of Granada, Granada, Spain
| | - Nicolas Robinson-Garcia
- EC3 Research Group, Department of Information and Communication Sciences, Faculty of Communication and Documentation, University of Granada, Granada, Spain
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COVID-19 and the social organization of knowledge in Wikipedia: a study of social representations. JOURNAL OF DOCUMENTATION 2021. [DOI: 10.1108/jd-01-2021-0006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
PurposeThe paper aims to present the development of conceptualization of coronavirus disease 2019 (COVID-19) based on associations with other articles on English edition of Wikipedia. The main goal of the paper is to study the social organization of knowledge about COVID-19 within the Wikipedia community of practice.Design/methodology/approachThe methodological approach taken in this study was based on the application of Moscovici's theory of social representations to Wikipedia's knowledge organization system (KOS). Internal links in the Wikipedia article about COVID-19 were considered anchors in its social representations. Each link in the introductory part of the article was considered an indicator of the semantic relationship between COVID-19 and other concepts from Wikipedia's knowledge base. The subject of this study was links extracted from all revisions of the COVID-19 article between February and September 2020. Qualitative and quantitative analyses were performed on these conceptual structures using both synchronic and diachronic approaches.FindingsIt was found that the evolution of anchors in the Wikipedia article on COVID-19 was in line with the mechanism of symbolic coping related to infectious disease. It went through stages of divergence, convergence and normalization. It shows that this mechanism governs the social organization of knowledge related to COVID-19 on Wikipedia.Originality/valueNo studies have been devoted to the image of COVID-19 as presented by the evolution of links in Wikipedia and its implications for knowledge organization (KO).
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Singh H, West R, Colavizza G. Wikipedia citations: A comprehensive data set of citations with
identifiers extracted from English Wikipedia. QUANTITATIVE SCIENCE STUDIES 2021. [DOI: 10.1162/qss_a_00105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Abstract
Wikipedia’s content is based on reliable and published sources. To this date, relatively little is known about what sources Wikipedia relies on, in part because extracting citations and identifying cited sources is challenging. To close this gap, we release Wikipedia Citations, a comprehensive data set of citations extracted from Wikipedia. We extracted29.3 million citations from 6.1 million English Wikipedia articles as of May 2020, and classified as being books, journal articles, or Web content. We were thus able to extract 4.0 million citations to scholarly publications with known identifiers—including DOI, PMC, PMID, and ISBN—and further equip an extra 261 thousand citations with DOIs from Crossref. As a result, we find that 6.7% of Wikipedia articles cite at least one journal article with an associated DOI, and that Wikipedia cites just 2% of all articles with a DOI currently indexed in the Web of Science. We release our code to allow the community to extend upon our work and update the data set in the future.
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